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• W2949288495 abstract "Nearest neighbor methods are a popular class of nonparametric estimators with several desirable properties, such as adaptivity to different distance scales in different regions of space. Prior work on convergence rates for nearest neighbor classification has not fully reflected these subtle properties. We analyze the behavior of these estimators in metric spaces and provide finite-sample, distribution-dependent rates of convergence under minimal assumptions. As a by-product, we are able to establish the universal consistency of nearest neighbor in a broader range of data spaces than was previously known. We illustrate our upper and lower bounds by introducing smoothness classes that are customized for nearest neighbor classification." @default.
• W2949288495 created "2019-06-27" @default.
• W2949288495 creator A5010790447 @default.
• W2949288495 creator A5045315890 @default.
• W2949288495 date "2014-06-30" @default.
• W2949288495 modified "2023-09-27" @default.
• W2949288495 title "Rates of Convergence for Nearest Neighbor Classification" @default.
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• W2949288495 hasPublicationYear "2014" @default.
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